论文标题
Griddly：游戏中AI研究的平台
Griddly: A platform for AI research in games
论文作者
论文摘要
近年来，游戏AI研究取得了巨大的突破，尤其是在增强学习（RL）中。尽管他们取得了成功，但基础游戏通常是通过自己的预设环境和游戏机制实现的，因此使研究人员难以创建不同的游戏环境。但是，测试RL代理对各种游戏环境的测试对于最近研究RL的概括并避免可能发生过度拟合的问题至关重要。在本文中，我们将Griddly作为游戏AI研究的新平台介绍，该平台提供了高度可配置的游戏，不同的观察者类型和有效的C ++核心引擎的独特组合。此外，我们提出了一系列基线实验，以研究RL剂的不同观察构构和概括能力的影响。
In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL). Despite their success, the underlying games are usually implemented with their own preset environments and game mechanics, thus making it difficult for researchers to prototype different game environments. However, testing the RL agents against a variety of game environments is critical for recent effort to study generalization in RL and avoid the problem of overfitting that may otherwise occur. In this paper, we present Griddly as a new platform for Game AI research that provides a unique combination of highly configurable games, different observer types and an efficient C++ core engine. Additionally, we present a series of baseline experiments to study the effect of different observation configurations and generalization ability of RL agents.
